* The mathematical technique to find a line (or curve) that best matches the points of data
that you collected is called regression.
* KNN (K-Nearest Neighbor) is a simple supervised classification algorithm we can use to assign a class to new data point.
It can be used for regression as well, KNN does not make any assumptions on the data distribution, hence it is non-parametric.
* Logistic Regression is a supervised machine learning algorithm used in binary classification.
* $pip install -r requirements.txt
* python 3.6.5
The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values (sample or population values) predicted by a model or an estimator and the values observed.
epsilon is the percentage of the time that the agent takes a randomly selected action rather than the action that is most likely to maximize reward given what it is known so far.
Multicollinearity occurs when independent variables in a regression model are correlated. This correlation is a problem because independent variables should be independent. If the degree of correlation between variables is high enough, it can cause problems when you fit the model and interpret the results.